Three broad areas of application have emerged over the years in computer natural language processing (NLP). These include text translation, document retrieval and routing, and information extraction. Practical text translation systems work primarily on the basis of pattern matching and mapping. Retrieval and routing systems are based primarily on keyword matching augmented by statistical and proximity measures and word manipulations based on morphology and synonymy. Information extraction systems vary more widely in terms of implementation algorithms. Some systems are based purely on keywords, statistics and pattern matching. However, most systems have some level of linguistic processing capability.
In the area of medical NLP, several academic research systems have been designed and built for the purpose of processing the information in physician notes. One area of basic research is classification of medical terms based on definition in context. Medical terminology, as does all natural language components, has ambiguities. The study of how to resolve ambiguities based on contextual clues is an important area of basic research.
Typical medical coding applications function simply as computerized look-up tools. Search terms retrieve candidate codes from on-line versions of standard coding references. Search terms are entered into pre-contextualized slots to further focus the search and improve accuracy. Coding from a full narrative of physician notes, however, is not yet available or even possible by conventional coding applications.
Medical applications related to processing of the full narrative of physician notes are largely related to retrieval and routing. For example, U.S. Pat. No. 5,809,476 to Ryan describes the use of NLP to apply diagnosis and procedure codes to physician notes. However, traditional NLP approaches for generating medical codes are inefficient and inaccurate. To survive and thrive in today's medical climate, medical records coding needs to produce reliable, consistent results.
Many modern solutions cannot parse large documents based on document structure. In particular, most cannot parse sentences in a manner that would permit learning of syntactic clues that affect term combination and disambiguation. In many systems, an overall approach for accepting or rejecting potential term combinations is lacking.
At present, no known solution exists for identifying the presence of relevant information in physician notes that an NLP system may be able to use for purposes of interpretation or extraction of information.
Also, no capability is known to exist to identify who, in the context of a physician's narrative, is describing a symptom, diagnosis, or procedure, and then use such information.
No known solution makes an assessment of the level of medical service based on the application of established rules for medical coding to the raw content of a physician's note.
Generally, work-flow evaluations are incomplete in that no solution (1) integrates demographic information with a physician note, (2) ensures completeness of a transcription of a physician note; and/or (3) provides capability for integrating human input to resolve problems of ambiguity or incomplete information.
The capability to attribute a statement to the correct source and to determine the affirmation or denial status of a statement is critical for proper and accurate billing (or even clinical) purposes. Also, medical service codes are based on factors other than the particular diagnosis or procedure(s) performed, such as for example, past medical history, family medical history, social habits of the patient, and extent and nature of the physical examination.
Accordingly, the inventors have determined that it is desirable to be able to automate medical diagnosis, procedure, and level of service (also known as evaluation and management (EM)) coding in a more efficient manner to provide a more reliable indicator of physician notes.